{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "033e837a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tsfresh import extract_features\n",
    "from tsfresh.utilities.dataframe_functions import impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24b82f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8100, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>co2</th>\n",
       "      <th>humidity_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-04 18:00:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.175000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.00</td>\n",
       "      <td>0.004745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-04 18:01:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>690.25</td>\n",
       "      <td>0.004741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-04 18:02:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>691.00</td>\n",
       "      <td>0.004739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-04 18:03:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>419.0</td>\n",
       "      <td>683.50</td>\n",
       "      <td>0.004751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-04 18:04:00</td>\n",
       "      <td>23.050</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>687.50</td>\n",
       "      <td>0.004734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                date  temperature   humidity  light     co2  \\\n",
       "0   1 2015-02-04 18:00:00       23.075  27.175000  419.0  688.00   \n",
       "1   1 2015-02-04 18:01:00       23.075  27.150000  419.0  690.25   \n",
       "2   1 2015-02-04 18:02:00       23.100  27.100000  419.0  691.00   \n",
       "3   1 2015-02-04 18:03:00       23.100  27.166667  419.0  683.50   \n",
       "4   1 2015-02-04 18:04:00       23.050  27.150000  419.0  687.50   \n",
       "\n",
       "   humidity_ratio  \n",
       "0        0.004745  \n",
       "1        0.004741  \n",
       "2        0.004739  \n",
       "3        0.004751  \n",
       "4        0.004734  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "\n",
    "X = pd.read_csv(\"occupancy.csv\", parse_dates=[\"date\"])\n",
    "\n",
    "print(X.shape)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8eb101fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(135,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "Name: occupancy, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load target\n",
    "\n",
    "y = pd.read_csv(\"occupancy_target.csv\", index_col=\"id\")[\"occupancy\"]\n",
    "\n",
    "print(y.shape)\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f84990a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plot sub-time series\n",
    "\n",
    "def plot_timeseries(n_id):\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(20, 10))\n",
    "\n",
    "    X[X[\"id\"] == n_id][\"temperature\"].plot(ax=axes[0, 0], title=\"temperature\")\n",
    "    X[X[\"id\"] == n_id][\"humidity\"].plot(ax=axes[0, 1], title=\"humidity\")\n",
    "    X[X[\"id\"] == n_id][\"light\"].plot(ax=axes[0, 2], title=\"light\")\n",
    "    X[X[\"id\"] == n_id][\"co2\"].plot(ax=axes[1, 0], title=\"co2\")\n",
    "    X[X[\"id\"] == n_id][\"humidity_ratio\"].plot(ax=axes[1, 1], title=\"humidity_ratio\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba2eb19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a time series where occupancy is 0\n",
    "\n",
    "plot_timeseries(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b3be208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a time series where occupancy is 1\n",
    "\n",
    "plot_timeseries(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f6a6967",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████████████████████████████████████████████████████████| 10/10 [00:14<00:00,  1.46s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(135, 789)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create features from one time series: light\n",
    "\n",
    "features = extract_features(X[[\"id\", \"light\"]], column_id=\"id\")\n",
    "\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "914b333e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>light__variance_larger_than_standard_deviation</th>\n",
       "      <th>light__has_duplicate_max</th>\n",
       "      <th>light__has_duplicate_min</th>\n",
       "      <th>light__has_duplicate</th>\n",
       "      <th>light__sum_values</th>\n",
       "      <th>light__abs_energy</th>\n",
       "      <th>light__mean_abs_change</th>\n",
       "      <th>light__mean_change</th>\n",
       "      <th>light__mean_second_derivative_central</th>\n",
       "      <th>light__median</th>\n",
       "      <th>...</th>\n",
       "      <th>light__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>light__permutation_entropy__dimension_7__tau_1</th>\n",
       "      <th>light__query_similarity_count__query_None__threshold_0.0</th>\n",
       "      <th>light__matrix_profile__feature_\"min\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"max\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"mean\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"median\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"25\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"75\"__threshold_0.98</th>\n",
       "      <th>light__mean_n_absolute_max__number_of_maxima_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2932.5</td>\n",
       "      <td>1228508.25</td>\n",
       "      <td>7.101695</td>\n",
       "      <td>-7.101695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.540075</td>\n",
       "      <td>0.63793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 789 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   light__variance_larger_than_standard_deviation  light__has_duplicate_max  \\\n",
       "1                                             1.0                       1.0   \n",
       "2                                             0.0                       1.0   \n",
       "3                                             0.0                       1.0   \n",
       "4                                             0.0                       1.0   \n",
       "5                                             0.0                       1.0   \n",
       "\n",
       "   light__has_duplicate_min  light__has_duplicate  light__sum_values  \\\n",
       "1                       1.0                   1.0             2932.5   \n",
       "2                       1.0                   1.0                0.0   \n",
       "3                       1.0                   1.0                0.0   \n",
       "4                       1.0                   1.0                0.0   \n",
       "5                       1.0                   1.0                0.0   \n",
       "\n",
       "   light__abs_energy  light__mean_abs_change  light__mean_change  \\\n",
       "1         1228508.25                7.101695           -7.101695   \n",
       "2               0.00                0.000000            0.000000   \n",
       "3               0.00                0.000000            0.000000   \n",
       "4               0.00                0.000000            0.000000   \n",
       "5               0.00                0.000000            0.000000   \n",
       "\n",
       "   light__mean_second_derivative_central  light__median  ...  \\\n",
       "1                                    0.0            0.0  ...   \n",
       "2                                    0.0            0.0  ...   \n",
       "3                                    0.0            0.0  ...   \n",
       "4                                    0.0            0.0  ...   \n",
       "5                                    0.0            0.0  ...   \n",
       "\n",
       "   light__permutation_entropy__dimension_6__tau_1  \\\n",
       "1                                        0.540075   \n",
       "2                                       -0.000000   \n",
       "3                                       -0.000000   \n",
       "4                                       -0.000000   \n",
       "5                                       -0.000000   \n",
       "\n",
       "   light__permutation_entropy__dimension_7__tau_1  \\\n",
       "1                                         0.63793   \n",
       "2                                        -0.00000   \n",
       "3                                        -0.00000   \n",
       "4                                        -0.00000   \n",
       "5                                        -0.00000   \n",
       "\n",
       "   light__query_similarity_count__query_None__threshold_0.0  \\\n",
       "1                                                NaN          \n",
       "2                                                NaN          \n",
       "3                                                NaN          \n",
       "4                                                NaN          \n",
       "5                                                NaN          \n",
       "\n",
       "   light__matrix_profile__feature_\"min\"__threshold_0.98  \\\n",
       "1                                                NaN      \n",
       "2                                                NaN      \n",
       "3                                                NaN      \n",
       "4                                                NaN      \n",
       "5                                                NaN      \n",
       "\n",
       "   light__matrix_profile__feature_\"max\"__threshold_0.98  \\\n",
       "1                                                NaN      \n",
       "2                                                NaN      \n",
       "3                                                NaN      \n",
       "4                                                NaN      \n",
       "5                                                NaN      \n",
       "\n",
       "   light__matrix_profile__feature_\"mean\"__threshold_0.98  \\\n",
       "1                                                NaN       \n",
       "2                                                NaN       \n",
       "3                                                NaN       \n",
       "4                                                NaN       \n",
       "5                                                NaN       \n",
       "\n",
       "   light__matrix_profile__feature_\"median\"__threshold_0.98  \\\n",
       "1                                                NaN         \n",
       "2                                                NaN         \n",
       "3                                                NaN         \n",
       "4                                                NaN         \n",
       "5                                                NaN         \n",
       "\n",
       "   light__matrix_profile__feature_\"25\"__threshold_0.98  \\\n",
       "1                                                NaN     \n",
       "2                                                NaN     \n",
       "3                                                NaN     \n",
       "4                                                NaN     \n",
       "5                                                NaN     \n",
       "\n",
       "   light__matrix_profile__feature_\"75\"__threshold_0.98  \\\n",
       "1                                                NaN     \n",
       "2                                                NaN     \n",
       "3                                                NaN     \n",
       "4                                                NaN     \n",
       "5                                                NaN     \n",
       "\n",
       "   light__mean_n_absolute_max__number_of_maxima_7  \n",
       "1                                      418.928571  \n",
       "2                                        0.000000  \n",
       "3                                        0.000000  \n",
       "4                                        0.000000  \n",
       "5                                        0.000000  \n",
       "\n",
       "[5 rows x 789 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a935c569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['light__variance_larger_than_standard_deviation',\n",
       " 'light__has_duplicate_max',\n",
       " 'light__has_duplicate_min',\n",
       " 'light__has_duplicate',\n",
       " 'light__sum_values',\n",
       " 'light__abs_energy',\n",
       " 'light__mean_abs_change',\n",
       " 'light__mean_change',\n",
       " 'light__mean_second_derivative_central',\n",
       " 'light__median',\n",
       " 'light__mean',\n",
       " 'light__length',\n",
       " 'light__standard_deviation',\n",
       " 'light__variation_coefficient',\n",
       " 'light__variance',\n",
       " 'light__skewness',\n",
       " 'light__kurtosis',\n",
       " 'light__root_mean_square',\n",
       " 'light__absolute_sum_of_changes',\n",
       " 'light__longest_strike_below_mean',\n",
       " 'light__longest_strike_above_mean',\n",
       " 'light__count_above_mean',\n",
       " 'light__count_below_mean',\n",
       " 'light__last_location_of_maximum',\n",
       " 'light__first_location_of_maximum',\n",
       " 'light__last_location_of_minimum',\n",
       " 'light__first_location_of_minimum',\n",
       " 'light__percentage_of_reoccurring_values_to_all_values',\n",
       " 'light__percentage_of_reoccurring_datapoints_to_all_datapoints',\n",
       " 'light__sum_of_reoccurring_values',\n",
       " 'light__sum_of_reoccurring_data_points',\n",
       " 'light__ratio_value_number_to_time_series_length',\n",
       " 'light__sample_entropy',\n",
       " 'light__maximum',\n",
       " 'light__absolute_maximum',\n",
       " 'light__minimum',\n",
       " 'light__benford_correlation',\n",
       " 'light__time_reversal_asymmetry_statistic__lag_1',\n",
       " 'light__time_reversal_asymmetry_statistic__lag_2',\n",
       " 'light__time_reversal_asymmetry_statistic__lag_3',\n",
       " 'light__c3__lag_1',\n",
       " 'light__c3__lag_2',\n",
       " 'light__c3__lag_3',\n",
       " 'light__cid_ce__normalize_True',\n",
       " 'light__cid_ce__normalize_False',\n",
       " 'light__symmetry_looking__r_0.0',\n",
       " 'light__symmetry_looking__r_0.05',\n",
       " 'light__symmetry_looking__r_0.1',\n",
       " 'light__symmetry_looking__r_0.15000000000000002',\n",
       " 'light__symmetry_looking__r_0.2',\n",
       " 'light__symmetry_looking__r_0.25',\n",
       " 'light__symmetry_looking__r_0.30000000000000004',\n",
       " 'light__symmetry_looking__r_0.35000000000000003',\n",
       " 'light__symmetry_looking__r_0.4',\n",
       " 'light__symmetry_looking__r_0.45',\n",
       " 'light__symmetry_looking__r_0.5',\n",
       " 'light__symmetry_looking__r_0.55',\n",
       " 'light__symmetry_looking__r_0.6000000000000001',\n",
       " 'light__symmetry_looking__r_0.65',\n",
       " 'light__symmetry_looking__r_0.7000000000000001',\n",
       " 'light__symmetry_looking__r_0.75',\n",
       " 'light__symmetry_looking__r_0.8',\n",
       " 'light__symmetry_looking__r_0.8500000000000001',\n",
       " 'light__symmetry_looking__r_0.9',\n",
       " 'light__symmetry_looking__r_0.9500000000000001',\n",
       " 'light__large_standard_deviation__r_0.05',\n",
       " 'light__large_standard_deviation__r_0.1',\n",
       " 'light__large_standard_deviation__r_0.15000000000000002',\n",
       " 'light__large_standard_deviation__r_0.2',\n",
       " 'light__large_standard_deviation__r_0.25',\n",
       " 'light__large_standard_deviation__r_0.30000000000000004',\n",
       " 'light__large_standard_deviation__r_0.35000000000000003',\n",
       " 'light__large_standard_deviation__r_0.4',\n",
       " 'light__large_standard_deviation__r_0.45',\n",
       " 'light__large_standard_deviation__r_0.5',\n",
       " 'light__large_standard_deviation__r_0.55',\n",
       " 'light__large_standard_deviation__r_0.6000000000000001',\n",
       " 'light__large_standard_deviation__r_0.65',\n",
       " 'light__large_standard_deviation__r_0.7000000000000001',\n",
       " 'light__large_standard_deviation__r_0.75',\n",
       " 'light__large_standard_deviation__r_0.8',\n",
       " 'light__large_standard_deviation__r_0.8500000000000001',\n",
       " 'light__large_standard_deviation__r_0.9',\n",
       " 'light__large_standard_deviation__r_0.9500000000000001',\n",
       " 'light__quantile__q_0.1',\n",
       " 'light__quantile__q_0.2',\n",
       " 'light__quantile__q_0.3',\n",
       " 'light__quantile__q_0.4',\n",
       " 'light__quantile__q_0.6',\n",
       " 'light__quantile__q_0.7',\n",
       " 'light__quantile__q_0.8',\n",
       " 'light__quantile__q_0.9',\n",
       " 'light__autocorrelation__lag_0',\n",
       " 'light__autocorrelation__lag_1',\n",
       " 'light__autocorrelation__lag_2',\n",
       " 'light__autocorrelation__lag_3',\n",
       " 'light__autocorrelation__lag_4',\n",
       " 'light__autocorrelation__lag_5',\n",
       " 'light__autocorrelation__lag_6',\n",
       " 'light__autocorrelation__lag_7',\n",
       " 'light__autocorrelation__lag_8',\n",
       " 'light__autocorrelation__lag_9',\n",
       " 'light__agg_autocorrelation__f_agg_\"mean\"__maxlag_40',\n",
       " 'light__agg_autocorrelation__f_agg_\"median\"__maxlag_40',\n",
       " 'light__agg_autocorrelation__f_agg_\"var\"__maxlag_40',\n",
       " 'light__partial_autocorrelation__lag_0',\n",
       " 'light__partial_autocorrelation__lag_1',\n",
       " 'light__partial_autocorrelation__lag_2',\n",
       " 'light__partial_autocorrelation__lag_3',\n",
       " 'light__partial_autocorrelation__lag_4',\n",
       " 'light__partial_autocorrelation__lag_5',\n",
       " 'light__partial_autocorrelation__lag_6',\n",
       " 'light__partial_autocorrelation__lag_7',\n",
       " 'light__partial_autocorrelation__lag_8',\n",
       " 'light__partial_autocorrelation__lag_9',\n",
       " 'light__number_cwt_peaks__n_1',\n",
       " 'light__number_cwt_peaks__n_5',\n",
       " 'light__number_peaks__n_1',\n",
       " 'light__number_peaks__n_3',\n",
       " 'light__number_peaks__n_5',\n",
       " 'light__number_peaks__n_10',\n",
       " 'light__number_peaks__n_50',\n",
       " 'light__binned_entropy__max_bins_10',\n",
       " 'light__index_mass_quantile__q_0.1',\n",
       " 'light__index_mass_quantile__q_0.2',\n",
       " 'light__index_mass_quantile__q_0.3',\n",
       " 'light__index_mass_quantile__q_0.4',\n",
       " 'light__index_mass_quantile__q_0.6',\n",
       " 'light__index_mass_quantile__q_0.7',\n",
       " 'light__index_mass_quantile__q_0.8',\n",
       " 'light__index_mass_quantile__q_0.9',\n",
       " 'light__cwt_coefficients__coeff_0__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_0__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_0__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_0__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_1__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_1__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_1__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_1__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_2__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_2__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_2__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_2__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_3__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_3__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_3__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_4__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_4__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_4__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_5__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_5__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_5__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_6__w_2__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_6__w_5__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_6__w_10__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_6__w_20__widths_(2, 5, 10, 20)',\n",
       " 'light__cwt_coefficients__coeff_7__w_2__widths_(2, 5, 10, 20)',\n",
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       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"mean\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"var\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"max\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"min\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"mean\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"var\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"max\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"min\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"mean\"',\n",
       " 'light__agg_linear_trend__attr_\"stderr\"__chunk_len_50__f_agg_\"var\"',\n",
       " 'light__augmented_dickey_fuller__attr_\"teststat\"__autolag_\"AIC\"',\n",
       " 'light__augmented_dickey_fuller__attr_\"pvalue\"__autolag_\"AIC\"',\n",
       " 'light__augmented_dickey_fuller__attr_\"usedlag\"__autolag_\"AIC\"',\n",
       " 'light__number_crossing_m__m_0',\n",
       " 'light__number_crossing_m__m_-1',\n",
       " 'light__number_crossing_m__m_1',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_0',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_1',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_2',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_3',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_4',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_5',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_6',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_7',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_8',\n",
       " 'light__energy_ratio_by_chunks__num_segments_10__segment_focus_9',\n",
       " 'light__ratio_beyond_r_sigma__r_0.5',\n",
       " 'light__ratio_beyond_r_sigma__r_1',\n",
       " 'light__ratio_beyond_r_sigma__r_1.5',\n",
       " 'light__ratio_beyond_r_sigma__r_2',\n",
       " 'light__ratio_beyond_r_sigma__r_2.5',\n",
       " 'light__ratio_beyond_r_sigma__r_3',\n",
       " 'light__ratio_beyond_r_sigma__r_5',\n",
       " 'light__ratio_beyond_r_sigma__r_6',\n",
       " 'light__ratio_beyond_r_sigma__r_7',\n",
       " 'light__ratio_beyond_r_sigma__r_10',\n",
       " 'light__count_above__t_0',\n",
       " 'light__count_below__t_0',\n",
       " 'light__lempel_ziv_complexity__bins_2',\n",
       " 'light__lempel_ziv_complexity__bins_3',\n",
       " 'light__lempel_ziv_complexity__bins_5',\n",
       " 'light__lempel_ziv_complexity__bins_10',\n",
       " 'light__lempel_ziv_complexity__bins_100',\n",
       " 'light__fourier_entropy__bins_2',\n",
       " 'light__fourier_entropy__bins_3',\n",
       " 'light__fourier_entropy__bins_5',\n",
       " 'light__fourier_entropy__bins_10',\n",
       " 'light__fourier_entropy__bins_100',\n",
       " 'light__permutation_entropy__dimension_3__tau_1',\n",
       " 'light__permutation_entropy__dimension_4__tau_1',\n",
       " 'light__permutation_entropy__dimension_5__tau_1',\n",
       " 'light__permutation_entropy__dimension_6__tau_1',\n",
       " 'light__permutation_entropy__dimension_7__tau_1',\n",
       " 'light__query_similarity_count__query_None__threshold_0.0',\n",
       " 'light__matrix_profile__feature_\"min\"__threshold_0.98',\n",
       " 'light__matrix_profile__feature_\"max\"__threshold_0.98',\n",
       " 'light__matrix_profile__feature_\"mean\"__threshold_0.98',\n",
       " 'light__matrix_profile__feature_\"median\"__threshold_0.98',\n",
       " 'light__matrix_profile__feature_\"25\"__threshold_0.98',\n",
       " 'light__matrix_profile__feature_\"75\"__threshold_0.98',\n",
       " 'light__mean_n_absolute_max__number_of_maxima_7']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fetaure names\n",
    "\n",
    "[f for f in features.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81eaa880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "339"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features with more than 50% NAN\n",
    "\n",
    "len([f for f in features.columns if features[f].isnull().mean() > 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abad387e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "277"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features with all NAN\n",
    "\n",
    "len([f for f in features.columns if features[f].isnull().mean() == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8149a9b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['light__mean', 'light__length', 'light__standard_deviation',\n",
       "       'light__variation_coefficient', 'light__variance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select 5 features to display in book recipe\n",
    "\n",
    "feats = features.columns[10:15]\n",
    "\n",
    "feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e358a0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>light__mean</th>\n",
       "      <th>light__length</th>\n",
       "      <th>light__standard_deviation</th>\n",
       "      <th>light__variation_coefficient</th>\n",
       "      <th>light__variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.875</td>\n",
       "      <td>60.0</td>\n",
       "      <td>134.485582</td>\n",
       "      <td>2.751623</td>\n",
       "      <td>18086.371875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   light__mean  light__length  light__standard_deviation  \\\n",
       "1       48.875           60.0                 134.485582   \n",
       "2        0.000           60.0                   0.000000   \n",
       "3        0.000           60.0                   0.000000   \n",
       "4        0.000           60.0                   0.000000   \n",
       "5        0.000           60.0                   0.000000   \n",
       "\n",
       "   light__variation_coefficient  light__variance  \n",
       "1                      2.751623     18086.371875  \n",
       "2                           NaN         0.000000  \n",
       "3                           NaN         0.000000  \n",
       "4                           NaN         0.000000  \n",
       "5                           NaN         0.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display some features (for book)\n",
    "\n",
    "features[feats].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff38bba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\fsml\\lib\\site-packages\\tsfresh\\utilities\\dataframe_functions.py:198: RuntimeWarning: The columns ['light__fft_coefficient__attr_\"real\"__coeff_31'\n",
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      " 'light__fft_coefficient__attr_\"abs\"__coeff_57'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_58'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_59'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_60'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_61'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_62'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_63'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_64'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_65'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_66'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_67'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_68'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_69'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_70'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_71'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_72'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_73'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_74'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_75'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_76'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_77'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_78'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_79'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_80'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_81'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_82'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_83'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_84'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_85'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_86'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_87'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_88'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_89'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_90'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_91'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_92'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_93'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_94'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_95'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_96'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_97'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_98'\n",
      " 'light__fft_coefficient__attr_\"abs\"__coeff_99'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_31'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_32'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_33'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_34'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_35'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_36'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_37'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_38'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_39'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_40'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_41'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_42'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_43'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_44'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_45'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_46'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_47'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_48'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_49'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_50'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_51'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_52'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_53'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_54'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_55'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_56'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_57'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_58'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_59'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_60'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_61'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_62'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_63'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_64'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_65'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_66'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_67'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_68'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_69'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_70'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_71'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_72'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_73'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_74'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_75'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_76'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_77'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_78'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_79'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_80'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_81'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_82'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_83'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_84'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_85'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_86'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_87'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_88'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_89'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_90'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_91'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_92'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_93'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_94'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_95'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_96'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_97'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_98'\n",
      " 'light__fft_coefficient__attr_\"angle\"__coeff_99'\n",
      " 'light__query_similarity_count__query_None__threshold_0.0'] did not have any finite values. Filling with zeros.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>light__variance_larger_than_standard_deviation</th>\n",
       "      <th>light__has_duplicate_max</th>\n",
       "      <th>light__has_duplicate_min</th>\n",
       "      <th>light__has_duplicate</th>\n",
       "      <th>light__sum_values</th>\n",
       "      <th>light__abs_energy</th>\n",
       "      <th>light__mean_abs_change</th>\n",
       "      <th>light__mean_change</th>\n",
       "      <th>light__mean_second_derivative_central</th>\n",
       "      <th>light__median</th>\n",
       "      <th>...</th>\n",
       "      <th>light__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>light__permutation_entropy__dimension_7__tau_1</th>\n",
       "      <th>light__query_similarity_count__query_None__threshold_0.0</th>\n",
       "      <th>light__matrix_profile__feature_\"min\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"max\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"mean\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"median\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"25\"__threshold_0.98</th>\n",
       "      <th>light__matrix_profile__feature_\"75\"__threshold_0.98</th>\n",
       "      <th>light__mean_n_absolute_max__number_of_maxima_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2932.500000</td>\n",
       "      <td>1.228508e+06</td>\n",
       "      <td>7.101695</td>\n",
       "      <td>-7.101695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.540075</td>\n",
       "      <td>0.637930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>418.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9302.666667</td>\n",
       "      <td>3.795074e+06</td>\n",
       "      <td>8.525424</td>\n",
       "      <td>7.101695</td>\n",
       "      <td>0.060345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.661757</td>\n",
       "      <td>1.768683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.072107</td>\n",
       "      <td>4.946542</td>\n",
       "      <td>2.718038</td>\n",
       "      <td>2.713749</td>\n",
       "      <td>1.897205</td>\n",
       "      <td>3.581291</td>\n",
       "      <td>422.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 789 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     light__variance_larger_than_standard_deviation  light__has_duplicate_max  \\\n",
       "1                                               1.0                       1.0   \n",
       "2                                               0.0                       1.0   \n",
       "3                                               0.0                       1.0   \n",
       "4                                               0.0                       1.0   \n",
       "5                                               0.0                       1.0   \n",
       "..                                              ...                       ...   \n",
       "131                                             0.0                       1.0   \n",
       "132                                             0.0                       1.0   \n",
       "133                                             0.0                       1.0   \n",
       "134                                             0.0                       1.0   \n",
       "135                                             1.0                       0.0   \n",
       "\n",
       "     light__has_duplicate_min  light__has_duplicate  light__sum_values  \\\n",
       "1                         1.0                   1.0        2932.500000   \n",
       "2                         1.0                   1.0           0.000000   \n",
       "3                         1.0                   1.0           0.000000   \n",
       "4                         1.0                   1.0           0.000000   \n",
       "5                         1.0                   1.0           0.000000   \n",
       "..                        ...                   ...                ...   \n",
       "131                       1.0                   1.0           0.000000   \n",
       "132                       1.0                   1.0           0.000000   \n",
       "133                       1.0                   1.0           0.000000   \n",
       "134                       1.0                   1.0           0.000000   \n",
       "135                       1.0                   1.0        9302.666667   \n",
       "\n",
       "     light__abs_energy  light__mean_abs_change  light__mean_change  \\\n",
       "1         1.228508e+06                7.101695           -7.101695   \n",
       "2         0.000000e+00                0.000000            0.000000   \n",
       "3         0.000000e+00                0.000000            0.000000   \n",
       "4         0.000000e+00                0.000000            0.000000   \n",
       "5         0.000000e+00                0.000000            0.000000   \n",
       "..                 ...                     ...                 ...   \n",
       "131       0.000000e+00                0.000000            0.000000   \n",
       "132       0.000000e+00                0.000000            0.000000   \n",
       "133       0.000000e+00                0.000000            0.000000   \n",
       "134       0.000000e+00                0.000000            0.000000   \n",
       "135       3.795074e+06                8.525424            7.101695   \n",
       "\n",
       "     light__mean_second_derivative_central  light__median  ...  \\\n",
       "1                                 0.000000            0.0  ...   \n",
       "2                                 0.000000            0.0  ...   \n",
       "3                                 0.000000            0.0  ...   \n",
       "4                                 0.000000            0.0  ...   \n",
       "5                                 0.000000            0.0  ...   \n",
       "..                                     ...            ...  ...   \n",
       "131                               0.000000            0.0  ...   \n",
       "132                               0.000000            0.0  ...   \n",
       "133                               0.000000            0.0  ...   \n",
       "134                               0.000000            0.0  ...   \n",
       "135                               0.060345            0.0  ...   \n",
       "\n",
       "     light__permutation_entropy__dimension_6__tau_1  \\\n",
       "1                                          0.540075   \n",
       "2                                         -0.000000   \n",
       "3                                         -0.000000   \n",
       "4                                         -0.000000   \n",
       "5                                         -0.000000   \n",
       "..                                              ...   \n",
       "131                                       -0.000000   \n",
       "132                                       -0.000000   \n",
       "133                                       -0.000000   \n",
       "134                                       -0.000000   \n",
       "135                                        1.661757   \n",
       "\n",
       "     light__permutation_entropy__dimension_7__tau_1  \\\n",
       "1                                          0.637930   \n",
       "2                                         -0.000000   \n",
       "3                                         -0.000000   \n",
       "4                                         -0.000000   \n",
       "5                                         -0.000000   \n",
       "..                                              ...   \n",
       "131                                       -0.000000   \n",
       "132                                       -0.000000   \n",
       "133                                       -0.000000   \n",
       "134                                       -0.000000   \n",
       "135                                        1.768683   \n",
       "\n",
       "     light__query_similarity_count__query_None__threshold_0.0  \\\n",
       "1                                                  0.0          \n",
       "2                                                  0.0          \n",
       "3                                                  0.0          \n",
       "4                                                  0.0          \n",
       "5                                                  0.0          \n",
       "..                                                 ...          \n",
       "131                                                0.0          \n",
       "132                                                0.0          \n",
       "133                                                0.0          \n",
       "134                                                0.0          \n",
       "135                                                0.0          \n",
       "\n",
       "     light__matrix_profile__feature_\"min\"__threshold_0.98  \\\n",
       "1                                             1.072107      \n",
       "2                                             1.072107      \n",
       "3                                             1.072107      \n",
       "4                                             1.072107      \n",
       "5                                             1.072107      \n",
       "..                                                 ...      \n",
       "131                                           1.072107      \n",
       "132                                           1.072107      \n",
       "133                                           1.072107      \n",
       "134                                           1.072107      \n",
       "135                                           1.072107      \n",
       "\n",
       "     light__matrix_profile__feature_\"max\"__threshold_0.98  \\\n",
       "1                                             4.946542      \n",
       "2                                             4.946542      \n",
       "3                                             4.946542      \n",
       "4                                             4.946542      \n",
       "5                                             4.946542      \n",
       "..                                                 ...      \n",
       "131                                           4.946542      \n",
       "132                                           4.946542      \n",
       "133                                           4.946542      \n",
       "134                                           4.946542      \n",
       "135                                           4.946542      \n",
       "\n",
       "     light__matrix_profile__feature_\"mean\"__threshold_0.98  \\\n",
       "1                                             2.718038       \n",
       "2                                             2.718038       \n",
       "3                                             2.718038       \n",
       "4                                             2.718038       \n",
       "5                                             2.718038       \n",
       "..                                                 ...       \n",
       "131                                           2.718038       \n",
       "132                                           2.718038       \n",
       "133                                           2.718038       \n",
       "134                                           2.718038       \n",
       "135                                           2.718038       \n",
       "\n",
       "     light__matrix_profile__feature_\"median\"__threshold_0.98  \\\n",
       "1                                             2.713749         \n",
       "2                                             2.713749         \n",
       "3                                             2.713749         \n",
       "4                                             2.713749         \n",
       "5                                             2.713749         \n",
       "..                                                 ...         \n",
       "131                                           2.713749         \n",
       "132                                           2.713749         \n",
       "133                                           2.713749         \n",
       "134                                           2.713749         \n",
       "135                                           2.713749         \n",
       "\n",
       "     light__matrix_profile__feature_\"25\"__threshold_0.98  \\\n",
       "1                                             1.897205     \n",
       "2                                             1.897205     \n",
       "3                                             1.897205     \n",
       "4                                             1.897205     \n",
       "5                                             1.897205     \n",
       "..                                                 ...     \n",
       "131                                           1.897205     \n",
       "132                                           1.897205     \n",
       "133                                           1.897205     \n",
       "134                                           1.897205     \n",
       "135                                           1.897205     \n",
       "\n",
       "     light__matrix_profile__feature_\"75\"__threshold_0.98  \\\n",
       "1                                             3.581291     \n",
       "2                                             3.581291     \n",
       "3                                             3.581291     \n",
       "4                                             3.581291     \n",
       "5                                             3.581291     \n",
       "..                                                 ...     \n",
       "131                                           3.581291     \n",
       "132                                           3.581291     \n",
       "133                                           3.581291     \n",
       "134                                           3.581291     \n",
       "135                                           3.581291     \n",
       "\n",
       "     light__mean_n_absolute_max__number_of_maxima_7  \n",
       "1                                        418.928571  \n",
       "2                                          0.000000  \n",
       "3                                          0.000000  \n",
       "4                                          0.000000  \n",
       "5                                          0.000000  \n",
       "..                                              ...  \n",
       "131                                        0.000000  \n",
       "132                                        0.000000  \n",
       "133                                        0.000000  \n",
       "134                                        0.000000  \n",
       "135                                      422.000000  \n",
       "\n",
       "[135 rows x 789 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7cf8acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features,\n",
    "    y,\n",
    "    test_size=0.1,\n",
    "    random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb606d92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           1.00        14\n",
      "   macro avg       1.00      1.00      1.00        14\n",
      "weighted avg       1.00      1.00      1.00        14\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\fsml\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# train and evaluate logistic regression\n",
    "\n",
    "cls = LogisticRegression(random_state=10, C=0.01)\n",
    "cls.fit(X_train, y_train)\n",
    "\n",
    "print(classification_report(y_test, cls.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f1098ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████████████████████████████████████████████████████████| 10/10 [00:55<00:00,  5.55s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(135, 3945)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create features from all time series\n",
    "\n",
    "features = extract_features(\n",
    "    X,\n",
    "    column_id=\"id\",\n",
    "    impute_function=impute,\n",
    "    column_sort=\"date\",\n",
    ")\n",
    "\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5abb4bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test\n",
    "\n",
    "X_train, X_test, y_train, y_t5est = train_test_split(\n",
    "    features,\n",
    "    y,\n",
    "    test_size=0.1,\n",
    "    random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "35bb31bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95        11\n",
      "           1       0.75      1.00      0.86         3\n",
      "\n",
      "    accuracy                           0.93        14\n",
      "   macro avg       0.88      0.95      0.90        14\n",
      "weighted avg       0.95      0.93      0.93        14\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# train and evaluate logistic regression\n",
    "\n",
    "cls = LogisticRegression(random_state=10, C=0.000000000000001)\n",
    "cls.fit(X_train, y_train)\n",
    "\n",
    "print(classification_report(y_test, cls.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c9cbf2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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